Automatic Functional Differentiation in JAX
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Updated
Sep 18, 2025 - Python
Automatic Functional Differentiation in JAX
Convention-fixed literal-interval repair that closes the warped action at first variation and on the exact background.
Lagrangian mechanics and variational-calculus report artifacts.
First-variation boundary audit showing that the written GP-2 interval action fails literal boundary closure.
An intuitive derivation of smoothing splines from variational calculus, demonstrating their relationship to reproducing kernel Hilbert spaces (RKHS) and regularized neural networks.
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